Semantic frame parsing is an important research topic for understanding natural language that aims to construct a semantic frame that captures the semantics of user utterances/queries. In general, simple semantic frame parsing consists of two main tasks [1]}: detecting a related event for a given sentence, named a frame, and tagging all the words in the sentence associated with this event, named frame elements. Hence, this technique can also be understood as the combination of a sentence-level classification task and a sequence labeling task. Semantic frame parsing has been widely used for spoken language understanding (SLU) to develop personal AI assistants and chatbots. In SLU, two semantic frame parsing tasks are specified as intent detection and slot filling, which correspond to the frame and frame elements, respectively, in a conventional semantic frame parsing task. For a given utterance or sentence, the first task is to label the most likely intent of the utterance, and the second step is to assign a slot label to each of the tokens in the utterance. This information is then combined and further utilized by the downstream components in an SLU system. It is also common practice to jointly train two tasks in a semantic frame parsing system, i.e., to use one model to jointly perform intent classification and slot tagging [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}.
